Cross Project Actionable Warning Identification Using Hybrid PCA-MI with Machine Learning

Actionable Warning Identification (AWI) plays a significant role in improving the usability of static code analyzers.Recent studies show that actionable warning identification using machine learning is effective and works well within projects as long as there is sufficient amount of data available to train the models.Currently, the AWI classifier meets the issue of constrained performance on a small number of labeled warnings.We study the Cross Project Actionable Warning Identification (CP-AWI) for cases in which few or no data are available.CP-AWI uses the labeled data of a source project as training to construct a model for the target project.However, the difference in data distribution between the source project and the target project reduces the performance of warning identification.To solve this problem, this paper proposes a CP-AWI based on hybrid PCA-MI with machine learning.Firstly, Principal Component Analysis (PCA) is used to transform the features and adapt the feature dimensions between the source and target project datasets.Subsequently, Mutual Information (MI) is applied to measure the dependency between the input transformed features obtained from PCA and the target variable.Finally, machine learning models, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Nave Bayes (NB), are used to evaluate the proposed combination of the PCA-MI hybrid method for CP-AWI.The proposed CP-AWI system in our research, PCA-MI with RF, achieves the highest accuracy rate.This research is implemented on the eight open source java software project datasets (Ant, Cassandra, Commons.lang,Derby, Jmeter, Luence-solr, Maven, and Tomcat) and evaluated with standard evaluation metrics such as accuracy, Mean Squared Error (MAE) and Root Mean Squared Error (RMSE) and F-measure.The PCA-MI achieves an average 85% and 74% F-measure in compare to state-of-the-art methods indicates the effectiveness of the proposed approach.